Reshaping the On-Chain Game: How Monad's leading project, aPriori, led the trading revolution with AI, and the data contribution plan was launched simultaneously

Original source: aPriori

Heavy bets by top institutions such as Pantera Capital, YZi Lab, OKX Ventures, and more, aPriori is reconstructing the underlying beliefs of decentralized trading. The core members of the project come from Jump, Coinbase, Citadel Securities, and dYdX, combining on-chain native technology with practical experience in high-frequency trading on Wall Street, aPriori is building a next-generation transaction execution system on high-performance public chains, injecting a truly competitive trading infrastructure into DeFi.

aPriori is completely rewriting the on-chain transaction process: through AI-powered DEX aggregators and MEV-supported liquidity staking modules, aPriori integrates orders from order placement, matching to yield closed-loop, into a sustainable product system.

Following the team's launch of the AI-powered DEX aggregator Swapr last week, aPriori has set its sights on the "recognition brain" of on-chain transactions, known as the Order Flow Segmentation system. This system combines behavioral labeling, wallet clustering, AI analysis, and on-chain feedback mechanisms, with the goal of making every transaction smarter and fairer, avoiding "toxic flow" such as arbitrage slippage, and sending liquidity to where it should go most. It not only makes transactions smarter, but also makes the flow of the entire on-chain market more orderly and trusting.

"Understanding every transaction is the starting point for fair execution."

Order flow recognition is one of aPriori's core technologies, which analyzes transaction behavior, wallet history, and market reaction to determine whether a transaction is a normal user operation or a "toxic flow" such as arbitrage or pinching. Compared to traditional transactions that only look at whether the transaction is executed, this identification method can filter potential risks earlier, provide LPs with a safer counterparty, and improve path selection and execution fairness.

"Technology + Ecology: The Perfect Time to Belong to Monad"

Data characteristics vary among different public chain ecosystems: Solana has high-speed transactions and active users, but due to a large number of contracts being closed-source, the data available for training is limited; Although Ethereum and other EVM chains have open data, they are limited by performance bottlenecks, and the overall transaction behavior is conservative and the data density is low.

Monad achieves a rare balance between performance and transparency - it combines Solana-style high throughput with an aggressive trading style, while retaining the readability and openness brought by the EVM architecture. This provides aPriori with the ideal soil to build the next generation of order flow recognition models.

"User data is not just about engagement, it's about training the next generation of trading intelligence."

Community Data Contribution Program: To train AI to recognize transaction behavior more intelligently, aPriori has launched a community-engaged data contribution program. Each user can help the model better "understand" the on-chain world by completing the following simple actions.

· Binding wallets: Connect users' frequently used wallet addresses to provide a more complete view of behavior.

· Support Chain:Ethereum, BNB Chain, Monad testnet;

· Synchronized social accounts: Optionally link Twitter, Discord, etc. to add more identity clues;

· Check-in and task tracking: The dedicated panel displays user sign-in records, transaction behavior, and contribution progress.

This data can help the system determine which addresses belong to the same user and whether there are collaborative operations, improving AI's ability to identify transaction types and risks.

"How do you tell if a transaction contains toxic flow?"

In Swapr's core engine, each transaction is evaluated by an AI model before being confirmed, mainly referring to the following points:

· The transaction itself: buying and selling direction, currency path, gas, handling fees, slippage, etc.;

· Address history: transaction frequency, past behavior, asset changes;

· Market reaction: price action within 1 second to 24 hours after trading;

· Profit judgment: Whether the transaction is profitable at different times and whether it may hurt the LP.

The model identifies whether each transaction belongs to a "toxic flow", such as arbitrage or pinching, and determines its potential threat to the fairness of the system.

"The more complex the model, the better, but the more you understand trading, the more valuable it is."

From Rule Engine to AI Neural Network: aPriori is not limited to a single algorithm, but blends traditional models (XGBoost, LightGBM) with timing models (RNN, Transformer). The former is efficient at interpreting structured data, while the latter is good at capturing behavioral changes in time series.

Swapr ultimately adopts an Ensemble architecture, where different sub-models learn from their respective data dimensions and time windows, and after the fusion scores, they can respond to complex trading behaviors more accurately.

"Behind a transaction, who is conspiring to arbitrage?"

Arbitrage behavior is usually not done by a single wallet, but is the result of multiple addresses working together. By identifying these "behavior groups", the system can predict potential arbitrage groups and prevent "toxic flow" from concentrating on LPs.

"Make AI a part of trade execution"

With the abundance of training data, Swapr's identification system is becoming a core point of difference in DeFi routing. It not only brings better quotations, but also dynamically adjusts the direction of liquidity, protecting the interests of both users and LPs.

Founder Ray emphasized: "A true DeFi execution engine can understand, judge, and know how to protect the system. We hope that Swapr will be the first entry point for trading that can 'think'."

This article is from a contribution and does not represent the views of BlockBeats.